Extracting Fine-Grained Economic Events from Business News

Gilles Jacobs, Veronique Hoste


Abstract
Based on a recently developed fine-grained event extraction dataset for the economic domain, we present in a pilot study for supervised economic event extraction. We investigate how a state-of-the-art model for event extraction performs on the trigger and argument identification and classification. While F1-scores of above 50% are obtained on the task of trigger identification, we observe a large gap in performance compared to results on the benchmark ACE05 dataset. We show that single-token triggers do not provide sufficient discriminative information for a fine-grained event detection setup in a closed domain such as economics, since many classes have a large degree of lexico-semantic and contextual overlap.
Anthology ID:
2020.fnp-1.36
Volume:
Proceedings of the 1st Joint Workshop on Financial Narrative Processing and MultiLing Financial Summarisation
Month:
December
Year:
2020
Address:
Barcelona, Spain (Online)
Editors:
Dr Mahmoud El-Haj, Dr Vasiliki Athanasakou, Dr Sira Ferradans, Dr Catherine Salzedo, Dr Ans Elhag, Dr Houda Bouamor, Dr Marina Litvak, Dr Paul Rayson, Dr George Giannakopoulos, Nikiforos Pittaras
Venue:
FNP
SIG:
Publisher:
COLING
Note:
Pages:
235–245
Language:
URL:
https://aclanthology.org/2020.fnp-1.36
DOI:
Bibkey:
Cite (ACL):
Gilles Jacobs and Veronique Hoste. 2020. Extracting Fine-Grained Economic Events from Business News. In Proceedings of the 1st Joint Workshop on Financial Narrative Processing and MultiLing Financial Summarisation, pages 235–245, Barcelona, Spain (Online). COLING.
Cite (Informal):
Extracting Fine-Grained Economic Events from Business News (Jacobs & Hoste, FNP 2020)
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PDF:
https://aclanthology.org/2020.fnp-1.36.pdf